Authors: Vince Carey2, Another Author3.
Last modified: 1 Nov, 2020.
This document is a technical illustration of how workshop documents are authored and rendered. The content of this document will change completely as the workshop content is specified.
An example for a 45-minute workshop:
| Activity | Time |
|---|---|
| Brief intro to R/Rstudio | 10m |
| Biological context | 10m |
| Packages to be used | 10m |
| Analytical approach to the question | 15m |
| Simple exercises | 10m |
| Review | 5m |
List “big picture” student-centered workshop goals and learning objectives. Learning goals and objectives are related, but not the same thing. These goals and objectives will help some people to decide whether to attend the conference for training purposes, so please make these as precise and accurate as possible.
Learning goals are high-level descriptions of what participants will learn and be able to do after the workshop is over. Learning objectives, on the other hand, describe in very specific and measurable terms specific skills or knowledge attained. The Bloom’s Taxonomy may be a useful framework for defining and describing your goals and objectives, although there are others.
In the following chunk, we retrieve and render a table produced in Lambert et al. (2018).
lamtab = biocwk312::lamb_main_20201101
kable(lamtab[1:5,])| ID | Name | DBD | Is TF? | TF assessment | Binding mode | Motif status | Notes | Comments | Committee notes | MTW Notes | TRH Notes | SL notes | AJ notes | Disagree on Assessment | Disagree on Binding | Author1 | Assesment1 | Binding1 | Comment1 | Notes1 | Author2 | Assesment2 | Binding2 | Comment2 | Notes2 | Vaquerizas 2009 TF classification | CisBP considers it as a TF? | TFclass considers it as a TF? | TF-CAT classification | Is a GO TF | PDB |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ENSG00000137203 | TFAP2A | AP-2 | Yes | Known motif | 1 Monomer or homomultimer | High-throughput in vitro | NA | NA | NA | NA | NA | NA | NA | NA | NA | Sam Lambert | Has known motif | 1 Monomer or homomultimer | NA | NA | Yimeng Yin | Has known motif | 1 Monomer or homomultimer | NA | NA | a | Yes | Yes | TF Gene_DNA-Binding: sequence-specific_DNA Binding; Transactivation_PMIDS:11522791;15475956 | Yes | NA |
| ENSG00000008196 | TFAP2B | AP-2 | Yes | Known motif | 1 Monomer or homomultimer | High-throughput in vitro | NA | NA | NA | NA | NA | NA | NA | NA | NA | Matt Weirauch | Has known motif | 1 Monomer or homomultimer | NA | NA | Yimeng Yin | Has known motif | 1 Monomer or homomultimer | NA | NA | a | Yes | Yes | TF Gene_DNA-Binding: sequence-specific_DNA Binding; Transactivation_PMIDS:7555706 | Yes | NA |
| ENSG00000087510 | TFAP2C | AP-2 | Yes | Known motif | 1 Monomer or homomultimer | High-throughput in vitro | NA | NA | NA | NA | NA | NA | NA | NA | NA | Matt Weirauch | Has known motif | 1 Monomer or homomultimer | NA | NA | Yimeng Yin | Has known motif | 1 Monomer or homomultimer | NA | NA | a | Yes | Yes | No | Yes | NA |
| ENSG00000008197 | TFAP2D | AP-2 | Yes | Known motif | 1 Monomer or homomultimer | In vivo/Misc source | Only known motifs are from Transfac or HocoMoco - origin is uncertain | Binds the same GCCTGAGGC sequence as the other AP-2s (PMID: 24789576) | NA | NA | NA | NA | Binds the same GCCTGAGGC sequence as the other AP-2:s based on PMID: 24789576 | Disagree | NA | Arttu Jolma | Likely to be sequence specific TF | 1 Monomer or homomultimer | Binds GCCTGAGGC sequence based on PMID: 24789576 | NA | Sam Lambert | Has known motif | 1 Monomer or homomultimer | Source of Hocomoco motif is unclear | NA | a | Yes | Yes | No | Yes | NA |
| ENSG00000116819 | TFAP2E | AP-2 | Yes | Known motif | 1 Monomer or homomultimer | High-throughput in vitro | NA | NA | NA | NA | NA | NA | NA | NA | NA | Sam Lambert | Has known motif | 1 Monomer or homomultimer | NA | NA | Laura Campitelli | Has known motif | 1 Monomer or homomultimer | NA | NA | a | Yes | Yes | TF Gene_DNA-Binding: sequence-specific_DNA Binding_PMIDS:14572467 | Yes | NA |
When rendered in HTML, the table is searchable, thanks to ‘DT::datatable’.
Note that each transcription factor is accompanied by an Ensembl identifier.
We ‘wrap’ the identifiers to create a hyperlink, and then render using the ‘escape=FALSE’ setting for ‘datatable’.
usw_pref = "https://uswest.ensembl.org/Homo_sapiens/Gene/Summary?g="
wr_ens = function(x, pref) {
paste0("<A href='", pref, x, "'>", x, "</A>")
}
head(wr_ens(lamtab$ID, usw_pref)) # test
#> [1] "<A href='https://uswest.ensembl.org/Homo_sapiens/Gene/Summary?g=ENSG00000137203'>ENSG00000137203</A>"
#> [2] "<A href='https://uswest.ensembl.org/Homo_sapiens/Gene/Summary?g=ENSG00000008196'>ENSG00000008196</A>"
#> [3] "<A href='https://uswest.ensembl.org/Homo_sapiens/Gene/Summary?g=ENSG00000087510'>ENSG00000087510</A>"
#> [4] "<A href='https://uswest.ensembl.org/Homo_sapiens/Gene/Summary?g=ENSG00000008197'>ENSG00000008197</A>"
#> [5] "<A href='https://uswest.ensembl.org/Homo_sapiens/Gene/Summary?g=ENSG00000116819'>ENSG00000116819</A>"
#> [6] "<A href='https://uswest.ensembl.org/Homo_sapiens/Gene/Summary?g=ENSG00000117713'>ENSG00000117713</A>"
lamtab2 = lamtab
lamtab2$ID = wr_ens(lamtab2$ID, usw_pref)
DT::datatable(lamtab2, escape=FALSE)
#> Warning in instance$preRenderHook(instance): It seems your data is too big
#> for client-side DataTables. You may consider server-side processing: https://
#> rstudio.github.io/DT/server.htmlThis is all taken care of for the user of the TFutils package in the function ‘browse_lambert_main()’, which also includes links for PubMed IDs scattered in Lambert’s published Excel table.
Exercise: How many records mention transcription factor ‘YY1’?
Visualization of genomic data is closely linked to annotation. Annotation can be unwieldy and cumbersome to interrogate. We have collected some slices of genomic data and annotation to help with exploration of new approaches to interactive visualization in reports.
Reference positions of transcripts. We have information on a selection of transcripts on chromosome 17 in the vicinity of gene ORMDL3.
head(biocwk312::txdata_near_ormdl3,3)
#> GRanges object with 3 ranges and 4 metadata columns:
#> seqnames ranges strand | tx_id
#> <Rle> <IRanges> <Rle> | <character>
#> ENSE00003715979 17 37924458-37924570 + | ENST00000620683
#> ENSE00003732795 17 37977972-37979038 - | ENST00000618047
#> ENSE00003734132 17 37978470-37979038 - | ENST00000617125
#> gene_name gene_id type
#> <character> <character> <factor>
#> ENSE00003715979 TBC1D3D ENSG00000274419 exon
#> ENSE00003732795 TBC1D3L ENSG00000274512 exon
#> ENSE00003734132 TBC1D3C ENSG00000278299 exon
#> -------
#> seqinfo: 1 sequence from GRCh38 genomePositions and annotation of GWAS hits. We used the gwascat package to get a copy of the EBI/EMBL GWAS catalog on 1 November 2020, and limited the information to records pertaining to the interval 38-43 Mb on chr17. We also limited the number of metadata fields on the hits.
names(S4Vectors::mcols(biocwk312::hits_near_ormdl3_trunc10))
#> [1] "DISEASE/TRAIT" "STRONGEST SNP-RISK ALLELE"
#> [3] "SNPS" "RISK ALLELE FREQUENCY"
#> [5] "DATE ADDED TO CATALOG" "PUBMEDID"
#> [7] "REPORTED GENE(S)" "PVALUE_MLOG"
#> [9] "value"
head(biocwk312::hits_near_ormdl3_trunc10, 3)
#> GRanges object with 3 ranges and 9 metadata columns:
#> seqnames ranges strand | DISEASE/TRAIT
#> <Rle> <IRanges> <Rle> | <character>
#> [1] 17 39766006 * | Primary biliary chol..
#> [2] 17 39965740 * | Asthma
#> [3] 17 39905943 * | Asthma
#> STRONGEST SNP-RISK ALLELE SNPS RISK ALLELE FREQUENCY
#> <character> <character> <numeric>
#> [1] rs907092-A rs907092 0.45
#> [2] rs3894194-A rs3894194 0.45
#> [3] rs2305480-G rs2305480 0.55
#> DATE ADDED TO CATALOG PUBMEDID REPORTED GENE(S) PVALUE_MLOG value
#> <Date> <numeric> <character> <numeric> <numeric>
#> [1] 2009-06-21 19458352 IKZF3 5.09691 5.09691
#> [2] 2010-10-14 20860503 GSDMA 8.30103 8.30103
#> [3] 2010-10-14 20860503 GSDMB 7.00000 7.00000
#> -------
#> seqinfo: 24 sequences from GRCh38 genomeAn interactive visualization of GWAS hits and nearby genes.
We’ll use the TnT package along with some functions built for the workshop to visualize GWAS hit locations in the context of transcripts.
The display below is interactive. Clicking the mouse or trackpad near the middle will zoom in. A mouse wheel will also control zoom. At greater magnifications, annotations will be displayed, or you can click on a feature to get textual annotation. You can drag the display left or right after magnification. To restore initial state, reload the page in the browser.
# first setup transcripts
trxview = TxTrackFromGRanges(biocwk312::txdata_near_ormdl3,
label = "Transcript\n Structure",
color = "grey2",height = 300)
trxview = reset_tooltip(trxview)
trxview = reset_color(trxview)
trxview = reset_display_label(trxview)
# second setup GWAS hits
hitview = TnT::PinTrack(biocwk312::hits_near_ormdl3_trunc10, color="blue")
# render
TnTGenome(list(hitview, trxview), view.range=GRanges("17", IRanges(39.5e6, 40.5e6)))Exercise: There is a single GWAS hit on chr17 between positions 39,932,000 and 39,934,000. Use zoom and drag to find it. What is the rs number for the SNP, what is the disease/trait with which this SNP is associated, and what is the risk allele frequency? These can be determined by clicking on the head of the blue pin plotted in the interval given.
Lambert, Samuel A., Arttu Jolma, Laura F. Campitelli, Pratyush K. Das, Yimeng Yin, Mihai Albu, Xiaoting Chen, Jussi Taipale, Timothy R. Hughes, and Matthew T. Weirauch. 2018. “The Human Transcription Factors.” Cell 172 (4): 650–65. https://doi.org/10.1016/j.cell.2018.01.029.